Predict F1 Race Winners with OpenAI GPT-4o, Historical Data & Slack Alerts — n8n ワークフロー

高 複雑度⏰ スケジュール27個のノード🏷️ Miscellaneous作成者：Cheng Siong Chin

概要

How It Works Every day at 8 AM, the workflow automatically retrieves the latest F1 data—including driver standings, qualifying results, race schedules, and circuit information. All sources are merged into a unified dataset, and driver performance metrics are computed using historical trends. An AI agent, enhanced with vectorized race history, evaluates patterns and generates race-winner predictions. When the confidence score exceeds the defined threshold, the system pushes an automated Slack ale

使用ノード

Google SheetsHTTP RequestPostgresSlackCodeAI AgentEmbeddings OpenAIOpenAI Chat ModelRecursive Character Text SplitterCode ToolSimple Vector StoreDefault Data Loader

ワークフロープレビュー

How It Works
Every day at 8 AM, the workflow automatically retrieves
Setup Steps
1. Update the workflow configuration with: `newsApiUrl`
2. Connect PostgreSQL using the schema:
prediction_date, pre
Use Cases
Applicable for sports analytics dashboards, betting pla
Customization
Extend by adding constructor predictions (modify AI pro
Benefits
Saves time by automating data collection, improves accu
Data Collection
Retrieves live F1 standings, historical results, qualif
Why: Fresh, multi-source data is vital. Standings show
Data Processing
Merges data and calculates 8 driver metrics, including
Why: Metrics reveal patterns raw data
AI Prediction
A LangChain agent (GPT-4o) analyzes the dataset with hi
Why: AI synthesizes complex data at s
Confidence Validation
Compares scores against a 0.75 threshold; high-confiden
Why: Reduces alert fatigue and ensures only reliable in
Driver Performance Metrics
Calculates 8 advanced metrics (podium rate, consistency
Historical Data Vectorization Pipeline
Transforms 3 years of F1 historical data into searchabl
docmodelembedtoolembedtooltooltool
⚡
S
Schedule Trigger
W
Workflow Configuration
Fetch Current F1 Season …
Fetch Driver Standings
Fetch Constructor Standi…
Merge F1 Data
F1 Prediction Agent
OpenAI Chat Model
R
Real-Time F1 News Tool
W
Weather Data Tool
F
Format Prediction Output
Fetch Historical Race Re…
Fetch Qualifying Results
Fetch Circuit Information
Historical Data Vector S…
OpenAI Embeddings
Document Loader
Recursive Text Splitter
Historical Data Retrieva…
Calculate Driver Perform…
C
Check Prediction Confide…
Store Prediction in Data…
Send High Confidence Alert
Log to Prediction Tracker
A
Aggregate Historical Data
Statistical Analysis Tool
OpenAI Embeddings1
27 nodes30 edges

仕組み

  1. 1

    トリガー

    このワークフローは スケジュール トリガーで開始します、定義されたスケジュールで実行。

  2. 2

    処理

    データは 27 個のノードを流れます, connecting agent, aggregate, code。

  3. 3

    出力

    ワークフローは自動化を完了し、設定された宛先に結果を配信します。

ノード詳細 (27)

GO

Google Sheets

googleSheets

#1
HT

HTTP Request

httpRequest

#2
PO

Postgres

postgres

#3
SL

Slack

slack

#4
CO

Code

code

#5
AI

AI Agent

n8n-nodes-langchain.agent

#6
EM

Embeddings OpenAI

n8n-nodes-langchain.embeddingsOpenAi

#7
OP

OpenAI Chat Model

n8n-nodes-langchain.lmChatOpenAi

#8
RE

Recursive Character Text Splitter

n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter

#9
CO

Code Tool

n8n-nodes-langchain.toolCode

#10
SI

Simple Vector Store

n8n-nodes-langchain.vectorStoreInMemory

#11
DE

Default Data Loader

n8n-nodes-langchain.documentDefaultDataLoader

#12

このワークフローのインポート方法

  1. 1右側の JSONをダウンロード ボタンをクリックしてワークフローファイルを保存します。
  2. 2n8nインスタンスを開き、ワークフロー → 新規 → ファイルからインポート に進みます。
  3. 3ダウンロードした predict-f1-race-winners-with-openai-gpt-4o-historical-data-slack-alerts ファイルを選択し、インポートをクリックします。
  4. 4各サービスノードの 認証情報（APIキー、OAuthなど）を設定します。
  5. 5ワークフローをテスト をクリックして動作確認し、有効化します。

またはn8nの JSONからインポート に直接貼り付け：

{ "name": "Predict F1 Race Winners with OpenAI GPT-4o, Historical Data & Slack Alerts", "nodes": [...], ...}

インテグレーション

agentaggregatecodedocumentdefaultdataloaderembeddingsopenaigooglesheetshttprequesthttprequesttooliflmchatopenaipostgresscheduletriggersetslacktextsplitterrecursivecharactertextsplittertoolcodevectorstoreinmemory

このワークフローを取得

ワンクリックでダウンロード＆インポート

JSONをダウンロードn8n.ioで見る
ノード27
複雑度high
トリガーscheduled
カテゴリMiscellaneous

作成者

Cheng Siong Chin

Cheng Siong Chin

@cschin

タグ

agentaggregatecodedocumentdefaultdataloaderembeddingsopenaigooglesheetshttprequesthttprequesttooliflmchatopenai
⚡

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